Start with a short introduction about what is going on in this document
Introduction
Introduction to PDCA, CAST and the methods contained within this document go here.
Background
Explain some background info that is needed. Change header colours to purple?
Arps Decline models used to forecast historically and which models will be analyzed here
Modified Hyperbolic
Explanation of that equation and a definition using code..?
Sample Production Data
Step through explanation of 2 stream sample data included. Plot both oil and gas production. Pick one well to
Plots
#Display chart of production data using plotly. Oil first.
oil_prod<-plot_ly(data = formated_production$CD, x = ~CD.Month, y = ~Oil.Rate, color = ~Unique.Well.ID, type='scatter',mode='lines')
oil_prod<-layout(oil_prod, xaxis = list(title = "Cal-Day Month"),yaxis = list(title = "Oil Rate (bbls/d)"), title = "Example Oil Production")
#Then gas
gas_prod<-plot_ly(data = formated_production$CD, x = ~CD.Month, y = ~Gas.Rate, color = ~Unique.Well.ID, type='scatter',mode='lines')
gas_prod<-layout(gas_prod, xaxis = list(title = "Cal-Day Month"),yaxis = list(title = "Gas Rate (Mcfd)"), title = "Example Gas Production")
combined<-subplot(oil_prod,gas_prod,nrows = 2,shareX = FALSE,titleX = FALSE,titleY = TRUE)
combined<-layout(combined,showlegend=FALSE,title="Oil & Gas Production",xaxis2=list(title="Cal-Day Month"))
combinedPDCA
Explain the background of what it is and how it can be helpful in forecasting, especially to handle uncertainty. Define PDCA here! Explain briefly bayesian methods of achieving this? Priors and posteriors
Monte Carlo Method
Explain the place of the monte carlo method as our first try at estimating the posterior ditribution, showing parameter distributions as defining our prior and explaining how they are each sampled
MCMC Method
Explain here and have links to jump around document? Or explain when we get to it?
CAST
Forecast Synthesis
Explain process of forecast sythesis here, and show how it is carried out for the modified hyperbolic. Show arps substitution, and explain
Loss Functions
How do we compare the synthesis forecasts against our actuals and determine what is a good fit and what is a poor fit? S
Soft L1 Loss
Define and apply soft L1 loss and show plot of a forecast against actuals colour by individual cost
PDCA Loss
Explain this loss function, where it’s from, and show a comparison of application to the Soft L1 loss
Cost Evaluation
Show evaluation of the loss function to come up with average cost parameters for each forecast Show plots coloured by avg cost
Best Fit
Show best fit using either loss function and explain how this may be a good fit for wells with good production history, we still want to create a probabilistic outcome for each well to capture forecast uncertainty
Show top 10% of functions, etc. using either loss function, and show that the Monte Carlo method returns adequate forecasts for good prod history, but not short history wells.. What’s next?? MCMC
Markov Chain Monte Carlo (MCMC)
Explain concept or link above if put there.
Burn-In
Show MC method as burn-in to determine best fit forecast to apply PDCA loss function to
Jumping function and distributions
Results
Probabilistic forecasts
Creation of P10, P50, Avg, and P90 forecasts
Darcy Redpath
Petronas Canada
dredpath@petronascanada.com